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레이더 위성영상을 활용한 침수피해 지역 파악 및 완화방안 연구
이명진 ( Moung Jin Lee ),명수정 ( Soo Jeong Myeong ),전성우 ( Seong Woo Jeon ),원중선 ( Joong Sun Won ) 한국환경연구원 2009 환경정책연구 Vol.8 No.2
This study applied satellite radar imagery to identify flooded areas and examined post-flooding conditions using time-series satellite radar imagery for the development of flood damage mitigation strategies. Using time-series satellite radar images, this study constructed a map delineating areas vulnerable to frequent flood damage. The extracted flooded areas were combined with reference land use maps to examine flood damage by land use type. Major landuse types with severe flood damage were agricultural and forested areas. The analysis of the damage conditions, in terms of land use, served as the basis for developing flood damage mitigation policies, in conjunction with land use planning. The policies for flood damage mitigation can be summarized as land use regulations, land use planning, and flood damage mapping. A preventive measure to minimize flood damage of properties, which regulates developing areas with high flooding potential, is highly recommended. Although this study suggested a number of policies for flood damage mitigation, they represent only a small number of possible policies useful for mitigating flood damage and other environmental problems. Based upon the results of this study, it may be concluded that satellite radar imagery has great potential in providing basic data for large-scale environmental problems such as flooding and oil spills. Nevertheless, further examinations should be conducted and the application of satellite radar imagery should be used to examine other environmental problems.
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이길준,김준엽,이광한,우사이먼성일 한국정보과학회 2023 정보과학회논문지 Vol.50 No.11
With the advancement of object detection models, it is possible to efficiently infer synthetic aperture radar (SAR) and electro-optical (EO) satellite images. However, conventional object detection models using horizontal bounding boxes (HBB) struggle to detect small and densely grouped objects in satellite images. To address this issue, this paper proposes E^2RDet. This algorithm effectively modifies the structure of the Yolov7 object detection model, enabling it to accurately detect objects represented by oriented bounding boxes (OBB) in SAR images. This algorithm improves the object detection model architecture and loss function to facilitate learning of an object's dynamic (orientation) posture. Using various training datasets, E^2RDet demonstrates performance improvements across three benchmark SAR datasets. This indicates that existing HBB object detection models can train and perform object detection on objects represented by OBBs.
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